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CMRH: A new method for solving nonsymmetric linear systems based on the Hessenberg reduction algorithm

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Abstract

The Generalized Minimal Residual (GMRES) method and the Quasi-Minimal Residual (QMR) method are two Krylov methods for solving linear systems. The main difference between these methods is the generation of the basis vectors for the Krylov subspace. The GMRES method uses the Arnoldi process while QMR uses the Lanczos algorithm for constructing a basis of the Krylov subspace.

In this paper we give a new method similar to QMR but based on the Hessenberg process instead of the Lanczos process. We call the new method the CMRH method. The CMRH method is less expensive and requires slightly less storage than GMRES. Numerical experiments suggest that it has behaviour similar to GMRES.

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References

  1. O. Axelsson, Conjugate gradient type methods for unsymmetric and inconsistent systems of linear equations, Linear Algebra Appl. 29 (1980) 1–16.

    Article  MATH  MathSciNet  Google Scholar 

  2. Z. Bai, D. Hu and L. Reichel, A Newton basis GMRES implementation, IMA J. Numer. Anal. 14 (1994) 563–581.

    MATH  MathSciNet  Google Scholar 

  3. A. Björck, Least squares methods, in: Handbook of Numerical Analysis, Vol. I: Finite Difference Methods – Solution of Equations in ℝ n, eds. P.G. Ciarlet and J.L. Lions (Elsevier/North-Holland, Amsterdam, 1990).

    Google Scholar 

  4. C. Brezinski, Other manifestations of the Schur complement, Linear Algebra Appl. 111 (1988) 231–247.

    Article  MATH  MathSciNet  Google Scholar 

  5. C. Brezinski, M. Redivo-Zaglia and H. Sadok, Avoiding breakdown and near breakdown in Lanczos type algorithms, Numer. Algorithms 1 (1991) 199–206.

    Article  MATH  MathSciNet  Google Scholar 

  6. C. Brezinski, M. Redivo-Zaglia and H. Sadok, A breakdown-free Lanczos type algorithm for solving linear systems, Numer. Math. 45 (1992) 361–376.

    MathSciNet  Google Scholar 

  7. P.N. Brown, A theoretical comparison of the Arnoldi and the GMRES algorithms, SIAM J. Sci. Statist. Comput. 12 (1991) 58–78.

    Article  MATH  MathSciNet  Google Scholar 

  8. S.C. Eisenstat, H.C. Elman and M.H. Schultz, Variational iterative methods for nonsymmetric systems of linear equations, SIAM J. Numer. Anal. 20 (1983) 345–357.

    Article  MATH  MathSciNet  Google Scholar 

  9. H.C. Elman, Iterative methods for large sparse nonsymmetric systems of linear equations, Ph.D. thesis, Computer Science Dept., Yale University, New Haven, CT (1982).

    Google Scholar 

  10. R. Freund, On Conjugate Gradient type methods and polynomial preconditioners for a class of complex non-Hermitian matrices, Numer. Math. 57 (1990) 285–312.

    Article  MATH  MathSciNet  Google Scholar 

  11. R. Freund, G.H. Golub and N.M. Nachtigal, Iterative solution of linear systems, Acta Numerica 1 (1992) 57–100.

    Article  MathSciNet  Google Scholar 

  12. R. Freund, M.H. Gutknecht and N.M. Nachtigal, An implementation of the look-ahead Lanczos algorithm for non-Hermitian matrices, SIAM J. Sci. Statist. Comput. 14 (1993) 137–158.

    Article  MATH  MathSciNet  Google Scholar 

  13. R. Freund and N.M. Nachtigal, QMR: A quasi-minimal residual method for non-Hermitian linear systems, Numer. Math. 60 (1991) 315–339.

    Article  MATH  MathSciNet  Google Scholar 

  14. R. Freund and N.M. Nachtigal, An implementation of the QMR method based on coupled two-term recurrences, SIAM J. Sci. Statist. Comput. 15 (1994) 313–337.

    Article  MATH  MathSciNet  Google Scholar 

  15. G. Golub and C.F. van Loan, Matrix Computations, 2nd ed. (Johns Hopkins Univ. Press, Baltimore, MD, 1989).

    MATH  Google Scholar 

  16. R.T. Gregory and D.L. Karney, A Collection of Matrices for Testing Computational Algorithms (Wiley, New York, 1969).

    Google Scholar 

  17. M.H. Gutknecht, A completed theory of the unsymmetric Lanczos process and related algorithms Part I, SIAM J. Matrix Anal. Appl. 13 (1992) 594–639.

    Article  MATH  MathSciNet  Google Scholar 

  18. K. Hessenberg, Behandlung der linearen Eigenwert-Aufgaben mit Hilfe der Hamilton–Cayleychen Gleichung, Darmstadt dissertation (1940).

  19. A.S. Householder, The Theory of Matrices in Numerical Analysis (Dover, New York, 1974).

    Google Scholar 

  20. Y. Huang and H.A. van der Vorst, Some observations on the convergence behaviour of GMRES, Delft University of Technology, Report 89-09 (1989).

  21. W.D. Joubert, Lanczos methods for the solution of nonsymmetric systems of linear equations, SIAM J. Matrix Anal. Appl. 13 (1992) 926–943.

    Article  MATH  MathSciNet  Google Scholar 

  22. W.D. Joubert and G.F. Carey, Parallelizable restarted iterative methods for nonsymmetric linear systems, Internat. J. Comput. Math. 44 (1992) 243–267.

    MATH  Google Scholar 

  23. B.N. Parlett, D.R. Taylor and Z.A. Liu, A look-ahead Lanczos algorithm for unsymmetric matrices, Math. Comp. 44 (1985) 105–124.

    Article  MATH  MathSciNet  Google Scholar 

  24. Y. Saad, Krylov subspace methods for solving large unsymmetric linear systems, Math. Comp. 37 (1981) 105–126.

    Article  MATH  MathSciNet  Google Scholar 

  25. Y. Saad and M.H. Schultz, GMRES: a generalized minimal residual algorithm for solving nonsymmetric linear systems, SIAM J. Sci. Statist. Comput. 7 (1986) 856–869.

    Article  MATH  MathSciNet  Google Scholar 

  26. K. Turner and H.F. Walker, Efficient hight accuracy solutions with GMRES(m), SIAM J. Sci. Statist. Comput. 13 (1992) 815–825.

    Article  MATH  MathSciNet  Google Scholar 

  27. H.A. van der Vorst, The convergence behaviour of some iterative solution methods, Delft University of Technology, Report 89-19 (1989).

  28. H.A. van der Vorst and C. Vuik, The superlinear convergence of GMRES, J. Comput. Appl. Math. 48 (1993) 327–341.

    Article  MATH  MathSciNet  Google Scholar 

  29. C. Vuik and H.A. van der Vorst, A comparison of some GMRES-like methods, Linear Algebra Appl. 160 (1992) 131–162.

    Article  MATH  MathSciNet  Google Scholar 

  30. H.F. Walker, Implementation of the GMRES method using Householder transformations, SIAM J. Sci. Statist. Comput. 9 (1988) 152–163.

    Article  MATH  MathSciNet  Google Scholar 

  31. H.F. Walker and L. Zhou, A simpler GMRES, Utah State University, Report 1/92/54 (1992).

  32. J.H. Wilkinson, The Algebraic Eigenvalue Problem (Clarendon Press, Oxford, UK, 1965).

    MATH  Google Scholar 

  33. D.M. Young and K.C. Jea, Generalized conjugate gradient acceleration of nonsymmetrizable iterative methods, Linear Algebra Appl. 34 (1980) 159–194.

    Article  MATH  MathSciNet  Google Scholar 

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Sadok, H. CMRH: A new method for solving nonsymmetric linear systems based on the Hessenberg reduction algorithm. Numerical Algorithms 20, 303–321 (1999). https://doi.org/10.1023/A:1019164119887

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